I want to thank the many readers who sent me their comments on PrOPS. In response to these suggestions, I made two changes when I recalculated the metric.

I controlled for the speed of the players using speed scores.

I broke the metric out into three predictive components (PrAVE, PrOBP, and PrSLG).

To proxy player speed, I took the average of the five speed scores referenced in Speed Scores and Reaching Base on Errors by Dan Levitt. I decided on using the mean after trying several other combinations of the individual speed scores. The improvement was real yet modest, improving a fit of the regression model just slightly. Breaking PrOPS out into it’s components is useful for identifying where players are under-performing.

3 Responses “PrOPS 2: Sabermetric Boogaloo”

Are you planning on publishing the regression coefficients and t-stats for the factors you examined in predicting OPS? I, for one, am curious about how much line drive % and G/F ratio impact one’s OPS.

I won’t be publishing the coefficients for a while, but I may in the future. I want to work out the kinks before making them public. And I will post them on THT for all to see when that time comes.

There does seem to be a slight bias towards underperformance. I’m not sure why that is, but I can think of a few reasons. It may have something to do with offense being down this year or that guys who receive very few at-bats are likely to systematically underperform — the model was estimated for hitters with many at-bats. And, of course, there’s the possibility that the model flat our sucks.

On Oakland, I’m thinking this underperformance probably has a lot to do with just some bad luck concentrated in one place. In my mind those numbers should give some hope to A’s fans.